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huge irrelevant search hits. In this paper, we propose an improved method for ranking of search results to reduce human efforts on locating interesting hits. The search results are re-ranked using adaptive user interest hierarchies (AUIH), which considers both investigator-defined keywords and user interest learnt from
Since short text is short of keywords and has sparse features, it brings about the similarity drift problem. The traditional clustering algorithms are usually ineffective and a waste of resources on dealing with short text stream. To overcome the above problems, this paper proposes an incremental clustering algorithm
title, keyword and link text information to represent the website. Heterogeneous classifiers are then built based on these different features. We propose a principled ensemble classification algorithm to combine the predicted results from different phishing detection classifiers. Hierarchical clustering technique has been
from these data collections. KeyGraph is a word co-occurrence based algorithm for topic modeling. We provide an extension for KeyGraph algorithm by incorporating WordNet hypernyms for Keywords in the data collection. Our results show that incorporating hypernyms for KeyGraph algorithm would result improved topic and
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.